add kia ai chat tools7

src/services/kiaai/chat.py

@@ -25,21 +25,18 @@ def _to_openai_messages(messages: list[BaseMessage]) -> list[dict]:
 
     Supports multimodal HumanMessages where content is already a list
     (e.g. [{"type": "text", "text": "..."}, {"type": "image_url", ...}]).
+    For plain text-only content lists, normalizes to a plain string.
     """
     result = []
     for msg in messages:
         if isinstance(msg, SystemMessage):
-            result.append({"role": "system", "content": msg.content})
+            result.append({"role": "system", "content": _normalize_content(msg.content)})
 
         elif isinstance(msg, HumanMessage):
-            # content may be a plain string or a multimodal list
-            if isinstance(msg.content, list):
-                result.append({"role": "user", "content": msg.content})
-            else:
-                result.append({"role": "user", "content": msg.content})
+            result.append({"role": "user", "content": _normalize_content(msg.content)})
 
         elif isinstance(msg, AIMessage):
-            result.append({"role": "assistant", "content": msg.content})
+            result.append({"role": "assistant", "content": _normalize_content(msg.content)})
 
         elif isinstance(msg, ToolMessage):
             result.append({
@@ -54,6 +51,28 @@ def _to_openai_messages(messages: list[BaseMessage]) -> list[dict]:
     return result
 
 
+def _normalize_content(content) -> str | list:
+    """
+    If content is a list containing only text-type items, return plain string.
+    If it contains images or other types, return the list as-is (multimodal).
+    """
+    if isinstance(content, str):
+        return content
+
+    if isinstance(content, list):
+        # Check if all items are plain text type
+        is_text_only = all(
+            isinstance(item, dict) and item.get("type") == "text"
+            for item in content
+        )
+        if is_text_only:
+            return " ".join(item.get("text", "") for item in content)
+        # Multimodal — keep as list
+        return content
+
+    return str(content)
+
+
 class KiaAIService:
     """
     Service for calling KIA AI models directly via httpx SSE streaming.
@@ -92,7 +111,17 @@ class KiaAIService:
 
         print(f"[KiaAI] POST {url}")
         print(f"[KiaAI] messages_count={len(messages)} | reasoning_effort={reasoning_effort}")
-        print(f"[KiaAI] payload (no content): { {k: v for k, v in payload.items() if k != 'messages'} }")
+
+        openai_messages = _to_openai_messages(messages)
+        for i, m in enumerate(openai_messages):
+            content_preview = m["content"][:120] if isinstance(m["content"], str) else str(m["content"])[:120]
+            print(f"[KiaAI] msg[{i}] role={m['role']} | content={content_preview!r}")
+
+        payload: dict = {"messages": openai_messages}
+
+        # Add reasoning_effort only for models that support it
+        if reasoning_effort and model_name in REASONING_MODELS:
+            payload["reasoning_effort"] = reasoning_effort
 
         full_content = ""
         usage: dict = {}
@@ -116,7 +145,6 @@ class KiaAIService:
                     if not line.strip():
                         continue
 
-                    # SSE lines: "data: {...}" or "data: [DONE]" or raw JSON
                     raw_data = line.removeprefix("data:").strip()
 
                     if raw_data == "[DONE]":
@@ -129,6 +157,12 @@ class KiaAIService:
                         print(f"[KiaAI] Skipping non-JSON line: {raw_data!r}")
                         continue
 
+                    # KIA server error inside stream
+                    if chunk.get("code") and chunk["code"] != 200:
+                        error_msg = chunk.get("msg", "Unknown KIA error")
+                        print(f"[KiaAI] Server error in stream: code={chunk['code']} msg={error_msg!r}")
+                        raise RuntimeError(f"KIA API error {chunk['code']}: {error_msg}")
+
                     print(f"[KiaAI] CHUNK keys={list(chunk.keys())} choices={chunk.get('choices')}")
 
                     choices = chunk.get("choices") or []